Synchronization between multimedia flows and social network threads

ABSTRACT

For controlling synchronization between a multimedia flow and a related social network thread, a device able to capture the multimedia flow and the social network thread selects (S 1 ) a time interval, determines (S 2 ) a list of multimedia keywords associated with the multimedia flow and determines (S 3 ) a list of social keywords associated with the social network thread, each multimedia keyword and each social keyword being associated with a timestamp. The device produces (S 4 ) a filtered list of filtered multimedia keywords by selecting distinctive multimedia keywords, each filtered multimedia keyword being associated with a set of similar social keywords, computes (S 5 ) a set of delays for at least one filtered multimedia keyword, each delay corresponding to the time difference between the timestamp of the filtered multimedia keyword and the timestamp of a similar social keyword associated with the filtered multimedia keyword, and determines (S 6 ) a drift for the time interval by means of at least one set of delays.

FIELD OF THE INVENTION

The present invention pertains to a mechanism for synchronizationbetween multimedia flows and social network threads.

BACKGROUND

Social media services and micro blogging applications are changing theway in which many people consume traditional broadcast media. Real-timebackchannel conversations are now common-place as people simultaneouslywatch TV whilst using social media to broadcast their own thoughts,sentiments, opinions and emotions related to what they are watching.

Yet, content analysis of social interactions such as social networkthreads may be a good approach for enriching knowledge about multimediacontents. The study of the content of social interactions associated toa multimedia flow can contribute to:

-   -   Increase knowledge about the multimedia content: people often        speak about what is happening in the multimedia content (events,        people, places, etc.). This can be used to enrich the multimedia        description, but also express various viewpoints, helpful to        categorize the acquired knowledge with different levels of        expressiveness.    -   Extend the content by creating semantic links to other pieces of        content (on the basis of similarities of interactions). This        will let people discover alternative multimedia resources        through other people's conversations.

Whatever the relevance of social media comments provided in relationshipwith a multimedia flow such a TV show, there is a natural delay betweenthe time at which the user decides to react to what he is watching andthe time at which he posts his reaction (delay due to cognition,analysis, reaction and also due to the system: entry mode, device,network).

This delay is somehow considered as not significant and almost ignoredby existing algorithms, which is probably relevant to index a whole twohours video sequence for example. But taking into account the delay isparticular mandatory:

-   -   to provide efficiently a new format of socialized multimedia        stream, containing both social and multimedia data,    -   to enable in-media indexation and navigation in order to offer        dynamic navigation within the content segments based on the        metadata structure.

Moreover, anchoring methods based only on time are not fullysatisfactory, due to uncertainties related to the moment social media isproduced, the type of platform used, or because social media is producedby bursts which depends on the very content of the media.

Therefore, there is a need to provide a new, more flexible mechanism forsynchronization of a broadcast multimedia content with the relatedsocial information.

SUMMARY

This summary is provided to introduce concepts related to the presentinventive subject matter. This summary is not intended to identifyessential features of the claimed subject matter nor is it intended foruse in determining or limiting the scope of the claimed subject matter.

In accordance with one embodiment, a method is provided for controllingsynchronization between a multimedia flow and a related social networkthread, the method comprising the following steps in a device able tocapture the multimedia flow and the social network thread:

selecting a time interval,

determining a list of multimedia keywords associated with a part of themultimedia flow corresponding to the time interval, each multimediakeyword being associated with a timestamp,

determining a list of social keywords associated with a part of thesocial network thread corresponding to the time interval, each socialkeyword being associated with a timestamp,

producing a filtered list of filtered multimedia keywords by selectingdistinctive multimedia keywords, each filtered multimedia keyword beingassociated with a set of similar social keywords,

computing a set of delays for at least one filtered multimedia keyword,each delay corresponding to the time difference between the timestamp ofthe filtered multimedia keyword and the timestamp of a similar socialkeyword associated with the filtered multimedia keyword,

determining a drift for the time interval by means of at least one setof delays.

Advantageously, the invention offers a system allowing to deeplysynchronize social network threads such as social data streams withmultimedia flows, where the mechanism is based on the combination of atimestamp and the semantic similarity between the social network threadand the multimedia flow.

Additionally, beyond simple viewing or sharing facilities, an effectivesynchronization process between streams will improve the accuracy of thein-media indexation process, enabling more complex usages, consisting inorganizing, transforming, decomposing and recomposing multimediacontents.

The benefit of this invention is to enable the synchronization of thebroadcast multimedia content with the related social informationregardless of the style (live or not live) of media consumption. Itprovides an innovative solution to anchor social data streams withmultimedia content by completing and enhancing a pure timestampsynchronization process.

The system can use temporal information and semantics analysis toassemble different multimedia sources, leveraging and easing theproduction and the consumption of both social media services and microblogging applications.

In an embodiment, the time interval is selected after having identifieda peak in a related social network thread, the time interval endcorresponding to the top of the peak.

In an embodiment, a peak is identified by a high variation of messagesvolume in the social network thread.

In an embodiment, a distinctive multimedia keyword has a low number ofoccurrences among the multimedia keywords in the corresponding timeinterval.

In an embodiment, a similar social keyword associated with a filteredmultimedia keyword is a keyword that has similar meaning or that issemantically close with respect to the filtered multimedia keyword.

In an embodiment, a delay is computed for each similar social keyword inthe list of social keywords.

In an embodiment, the drift for the time interval is determined if thenumber of filtered multimedia keywords is greater than a predefinedthreshold.

In an embodiment, the drift for the time interval is determined if thenumber of delays associated with the filtered multimedia keyword isgreater than a given threshold and if the entropy of the delaysdistribution is low.

In an embodiment, the drift for the time interval is determined by meansof global delays related to filtered multimedia keywords, a global delayrelated to a filtered multimedia keyword being an average of a givennumber of delays associated with the filtered multimedia keyword.

In an embodiment, the drift corresponds to an average of all globaldelays related to the filtered multimedia keywords.

In an embodiment, the list of multimedia keywords associated with themultimedia flow is determined after having extracted a multimediafragment of the multimedia flow corresponding to the time interval, andafter having analyzed content of the multimedia fragment to generate amultimedia fragment descriptor containing multimedia descriptiveelements.

In an embodiment, the list of social keywords associated with themultimedia flow is determined after having analyzed a set of data in thesocial network thread corresponding to the time interval to generate athread descriptor containing thread descriptive elements.

The invention also pertains to a device for controlling synchronizationbetween a multimedia flow and a related social network thread, thedevice being able to capture the multimedia flow and the social networkthread and comprising:

means for selecting a time interval,

means for determining a list of multimedia keywords associated with apart of the multimedia flow corresponding to the time interval, eachmultimedia keyword being associated with a timestamp,

means for determining a list of social keywords associated with a partof the social network thread corresponding to the time interval, eachsocial keyword being associated with a timestamp,

means for producing a filtered list of filtered multimedia keywords byselecting distinctive multimedia keywords, each filtered multimediakeyword being associated with a set of similar social keywords,

means for computing a set of delays for at least one filtered multimediakeyword, each delay corresponding to the time difference between thetimestamp of the filtered multimedia keyword and the timestamp of asimilar social keyword associated with the filtered multimedia keyword,

means for determining a drift for the time interval by means of at leastone set of delays.

The invention also pertains to a computer program capable of beingimplemented within a device, said program comprising instructions which,when the program is executed within said device, carry out steps of themethod according to the invention.

BRIEF DESCRIPTION OF THE FIGURES

The present invention and the benefits thereof shall be betterunderstood upon examining the description below, which makes referenceto the attached figures, in which:

FIG. 1 is a schematic block diagram of a communication system accordingto one embodiment of the invention for controlling a synchronizationbetween a multimedia flow and a related social network thread;

FIG. 2 illustrates an example of distribution of messages of a networksocial thread over time; and

FIG. 3 is an algorithm of a method for controlling a synchronizationbetween a multimedia flow and a related social network thread accordingto one embodiment of the invention.

The same reference number represents the same element or the same typeof element on all drawings.

DESCRIPTION OF EMBODIMENTS

With reference to FIG. 1, a communication system according to theinvention comprises a telecommunication network TN, a synchronizationdevice SD, at least one multimedia server MS and at least one socialnetwork server SNS.

The telecommunication network TN may be a wired or wireless network, ora combination of wired and wireless networks.

The telecommunication network TN can be a packet network, for example,an IP (“Internet Protocol”) high-speed network such as the Internet oran intranet, or even a company-specific private network.

As an introduction, a few terms and concepts that are helpful forunderstanding the invention are defined below.

The invention aims at offering a synchronization device SD intended forcomputing a set of delays over the time based on a semantic analysis ofa multimedia flow and social network thread related to a same multimediacontent.

A multimedia flow is provided by a multimedia server MS toward arequesting communication device through the telecommunication networkTN.

A multimedia flow can be an IPTV (Internet Protocol TeleVision) stream,or may be any other kind of video stream, an audio stream, a slideshow,a text stream such as a news title stream, and the like.

The multimedia flow can be a live stream that is broadcasted by themultimedia server MS and that is associated with an online or areal-time multimedia content. Alternatively, the multimedia flow can beassociated with offline content like an uploaded content used by adedicated service of the multimedia server.

A social network thread is an aggregation of essentially user postedmessages, possibly in reply to each other, and possibly with enclosed orembedded contents such as pictures, short videos, links, etc. The socialnetwork threads are stored in social network databases that are locatedon remote at least one social network server SNS forming a socialnetwork platform and are accessed via the internet.

For instance, social network threads may be annotations, like commentsfrom a person about one element of a multimedia content, orconversations between people about a multimedia content.

In a social network thread, each message is associated with a timestamp.

The timestamp associated to a message in a social network threadcorresponds to the absolute time that has been registered by the socialnetwork platform when receiving the message, which is different from thereference time that is the moment of time in the multimedia flow themessage is referring. Therefore, a strategy has to be put in place toperform a “drift correction” corresponding to the delay between the twodifferent times.

The synchronization device SD comprises a time selection module TSM, athread extraction module TEM, a multimedia extraction module MEM, asemantic analysis module SAM, an inspection module IM.

The time selection module TSM identifies peaks in social networkthreads.

To that end, the time selection module TSM selects a time window tobuild a map threads distribution over time.

As the amount and the distribution of data in threads can significantlyvary over time, the time selection module TSM can use filteringalgorithms like moving average or Gaussian filters in order to smoothout short-term fluctuations and to highlight longer-term trends orcycles. The threshold between short-term and long-term depends on theapplication, and the parameters of the moving average can be setaccordingly.

The time selection module TSM can use peak detection algorithms, forexample as described in reference “Simple Algorithms for Peak Detectionin Time-Series”, [in Proc. 1st Int. Conf. Advanced Data Analysis,Business Analytics and Intelligence, 2009] in order to identify peaks orspikes in a given time-series of social network threads.

The smoothing out of short-term fluctuations allows to delete smallvariations that can correspond to some kind of noise or anomalieswithout relation with the topic of the social network thread.

The time selection module TSM selects a small length multimedia flowsegment (e.g. 5 min, the value can be determined empirically) before an“event” or a peak identified by a consequent variation of messagesvolume, i.e. numbers of messages in a social network thread during thelength of the multimedia flow segment. In this way, each relevant peakidentifies a time interval or a multimedia flow segment to target.

In reference with FIG. 2, the messages of a network social thread arefor example “tweets” from Twitter™. The distribution of the number oftweets over time is shown. Each selected multimedia flow segment totarget is represented by a rectangle that coincides with a peak in thenumber of tweets. More precisely, the time of end of a segment coincideswith a peak. The peak means that an event has occurred, in other termsthat users have reacted to the previous multimedia flow segment byposting many messages.

Referring back to FIG. 1, the thread extraction module TEM and themultimedia extraction module MEM are responsible for gathering knowledgeinformation from raw data streams related to social network threads andmultimedia flows for targeted time intervals.

The thread extraction module TEM uses predictive methods for analyzingsocial network threads that are unstructured information and forgenerating descriptors for social network threads, potentially enrichedwith context information and extended with general-purpose thesauri ordictionaries. The thread extraction module TEM captures vectors ofkeywords from social network threads associated to a time interval.

The multimedia extraction module MEM uses feature descriptors in imageand video processing (or audio transcripts if available) to extractknowledge information for the multimedia flows. The multimediaextraction module MEM applies analysis techniques to extract a vector ofkeywords. In a possible embodiment for audio streams, the multimediaextraction module MEM can use technologies such as speech to text pluskeyword extraction for each sentence from a multimedia segment; forvideo streams, object recognition, person recognition techniques can beused.

More especially, the multimedia extraction module MEM is configured toextract parts of a multimedia content as fragments, and to isolatemultimedia fragments, containing frames or pictures, scenes or soundextracts.

The multimedia extraction module MEM can convert the multimediafragments in data usable for search queries or analysis, by extractingdescriptive elements and properties from the fragments. For example, ifthe fragments comprise a picture of an actor, the multimedia extractionmodule MEM may use a face recognition subunit to identify said actor,and isolate his name as a relevant descriptive element to use in searchqueries. Other possible data sources include object recognition on thepictures, music and sound recognition on the audio track, opticalcharacter recognition (OCR), speech-to-text conversion, chromaticanalysis and the like. The processes and techniques used to analyse themultimedia flow may be varying according to the nature of saidmultimedia flow.

For example, if the multimedia flow is a radio or more generally a soundstream, the multimedia extraction module MEM can run voice detectionmethods to identify the presence of a speech. In case a speech isdetected, the multimedia extraction module MEM uses a speech-to-textconversion subunit to transcript the sound in searchable words. If nospeech is detected, or in addition or in parallel to the speech-to-texttranscription, the multimedia extraction module MEM may use sound ormusic recognition subunits, for example to identify the title and artistof a played music track, or the name of a sound source (instrument,animal, etc.) and use them as keywords.

If the multimedia stream is a video stream or slide show, the multimediaextraction module MEM can use for example face or object recognitionsubunits, to identify people or objects on the multimedia fragment. Thenames obtained are then used as keywords.

The multimedia extraction module MEM may also use structural ordescriptive metadata. For example, on an IPTV program, the title, ashort synopsis and/or the cast and authors and producers may bebroadcasted by multiplexing the data with the multimedia content.

The thread extraction module TEM produces a list of social keywords fromthe analysis of descriptors for social network threads in the timeinterval. Each social keyword is associated with a timestamp.

The multimedia extraction module MEM produces a list of multimediakeywords from the analysis of descriptors for the fragment of multimediaflow corresponding to the time interval. Each multimedia keyword isassociated with a timestamp.

The semantic analysis module SAM is in charge of performing a semanticanalysis in the list of social keywords and the list of multimediakeywords.

For each time interval, a strict similarity measure is computed and usedfor the multimedia keywords. Any similarity metric of the literature canbe used as far as it satisfies the following constraints:

1) a very high similarity ratio for comparing the diversity of sets amultimedia keywords, using for example semantic analysis with respect tosocial keywords, and

2) multimedia keywords are previously filtered to only selectdistinctive words in the list of multimedia keywords.

As an example, a term frequency-inverse document frequency (TF-IDF) ordictionaries could be used to emphasize with a meaningful keywords whichhappen the less frequently. A strict similarity measure processtherefore provides possible co-occurrences with a high probability ofpertinence. Examples of similarities include e.g. the cosine similarityor a similarity such as the Sorensen-Dice coefficient.

The semantic analysis module SAM produces a filtered list of filteredmultimedia keywords after the strict similarity measure on the initiallist of multimedia keywords. For that, the semantic analysis module SAMselects distinctive multimedia keywords that have a high similarityratio with social keywords. For each distinctive multimedia keyword,“similar” keywords can appear several times in the social thread, andeach similar social keyword can have several occurrences.

The semantic analysis module SAM selects similar social keywords thatare present in the list of social keywords, and that are similar tofiltered multimedia keyword. For each filtered keyword in the filteredlist of filtered multimedia keywords, at least one similar socialkeyword in the list of social keywords is selected. Other occurrences ofa similar social keyword can appear in the list of social keywords; inthis case, these occurrences are also selected. It is assumed thatsimilar social keywords are keywords that have similar meaning or thatare semantically close. At the end, each filtered multimedia keyword isassociated with a set of similar social keywords.

The inspection module IM computes a delay value for a filteredmultimedia keyword by taking the time difference between the timestampof a similar social keyword in the list of social keywords and thetimestamp of the filtered multimedia keyword in the filtered list offiltered multimedia keywords. A delay value is computed for each similarsocial keyword in the list of social keyword.

Finally, the inspection module IM provides a final list of keyword, eachkeyword being associated with a list of delays, the number of delays perkeyword corresponding to the number of similar social keywords in thelist of social keywords.

The inspection module IM identifies if the time interval is qualified ornot. A time interval is qualified if a significant number (definedempirically) of relevant keywords is found.

To identify if a keyword is considered as relevant, the inspectionmodule IM checks two criteria:

1/ the numbers of delays associated with the keyword is greater than athreshold that is defined empirically; and

2/ the entropy (the variance for example) of the delays distribution islow. It means that delays calculated are more or less of the same orderof magnitude.

The inspection module IM can then calculate a global delay for thekeyword, for example as an average of all delays associated with thekeyword or as an average of a given number of delays associated with thekeyword.

If a time interval is considered as not relevant (i.e. there is nosignificant number of relevant keywords), the related results and thetime interval are ignored for future synchronization process.

The inspection module IM determines a final drift for this qualifiedtime interval by means of different global delays related to therelevant keywords. For example, the final drift corresponds to anaverage of all global delays related to the relevant keywords.

With reference to FIG. 3, a method for controlling a synchronizationbetween a multimedia flow and a related social network thread accordingto one embodiment of the invention comprises steps S1 to S6 executedwithin the communication system.

In step S1, the time selection module TSM of the synchronization deviceSD selects a time interval, corresponding to a multimedia flow segment.The time interval is identified after having identified at least onepeak in a related social network thread, the time interval endcorresponding to the top of a peak.

In step S2, the multimedia extraction module MEM determines a list ofmultimedia keywords associated with the part of the multimedia flowcorresponding to the time interval, each multimedia keyword beingassociated with a timestamp.

For example, a fragment of the multimedia flow corresponding to the timeinterval is analysed for extracting significant elements such as: face,object and character recognition for embedded images and videos, musicand sound recognition for audio tracks.

In step S3, the thread extraction module TEM determines a list of socialkeywords associated with the part of the social network threadcorresponding to the time interval, each social keyword being associatedwith a timestamp.

Recurrent keywords are listed with the number of times they occur in theconsidered social network thread.

In step S4, the semantic analysis module SAM filters the list ofmultimedia keywords by using a strict similarity measure on the keywordsin the list. The semantic analysis module SAM produces a filtered listof filtered multimedia keywords after the strict similarity measure onthe initial list of multimedia keywords.

To that end, the semantic analysis module SAM selects distinctivemultimedia keywords that have a high similarity ratio with socialkeywords. It is assumed that a distinctive multimedia keyword has arelative low number of occurrences among the set of multimedia keywordswith respect to other multimedia keywords.

The similarity ratio can be based on a semantic analysis.

For each filtered multimedia keyword in the filtered list, the semanticanalysis module SAM selects a set of social keywords in the list ofsocial keywords that are similar to the filtered multimedia keyword.Each filtered multimedia keyword is then associated with a set ofsimilar social keywords.

In step S5, the inspection module IM computes a delay value for afiltered multimedia keyword by taking the time difference between thetimestamp of the filtered multimedia keyword and the timestamp of asimilar social keyword associated with the filtered multimedia keyword.A delay value can be computed for each similar social keyword in thelist of social keywords. The inspection module IM computes a delay valuefor each filtered multimedia keyword.

The inspection module IM provides a final list of filtered multimediakeywords, each filtered multimedia keyword being associated with a listof delays, the number of delays per filtered multimedia keywordcorresponding to the number of similar social keywords in the list ofsocial keywords. Each filtered multimedia keyword in the final list canbe a multimedia keyword as each multimedia keyword has only oneoccurrence in the filtered list of multimedia keywords.

In step S6, the inspection module IM identifies if the time interval isqualified or not. A time interval is qualified if a significant numberof different relevant filtered multimedia keywords is found, i.e. if thenumber of different relevant filtered multimedia keywords is greaterthan a predefined threshold.

A filtered multimedia keyword is considered as relevant if the numbersof delays associated with the filtered multimedia keyword is greaterthan a given threshold and if the entropy of the delays distribution islow.

The inspection module IM calculates a global delay for the filteredmultimedia keyword, by means of the delays related to the filteredmultimedia keyword.

The inspection module IM determines a final drift for the qualified timeinterval by means of different delays related to the relevant filteredmultimedia keywords, more especially by means of different global delaysrelated to the relevant filtered multimedia keywords. The final drift isrepresentative of the average time difference between the multimediaflow and the related social network thread.

For example, the final drift value can be used to adjust the realtimestamp of tweets to be displayed together with a “replay-able”socialized multimedia content, i.e. a multimedia content that can bedownloaded and that is associated with a social network thread. Thus,the final drift value is used to synchronize the multimedia flow withthe social network thread.

The invention described here relates to a method and a device forcontrolling a synchronization between a multimedia flow and a relatedsocial network thread. According to one implementation of the invention,the steps of the invention are determined by the instructions of acomputer program incorporated into a device, such as the synchronizationdevice SD. The program comprises program instructions which, when saidprogram is loaded and executed within the device, carry out the steps ofthe method.

Consequently, the invention also applies to a computer program,particularly a computer program on or within an information medium,suitable to implement the invention. This program may use anyprogramming language, and be in the form of source code, object code, orintermediate code between source code and object code, such as in apartially compiled form, or in any other form desirable for implementingthe method according to the invention.

The invention claimed is:
 1. A method for controlling synchronizationbetween a multimedia flow and a related social network thread,comprising the following in a device able to capture the multimedia flowand the social network thread: selecting a time interval as a functionof a peak identified in a social network thread, producing a list ofmultimedia keywords associated with a part of the multimedia flowcorresponding to the time interval, each multimedia keyword beingassociated with a timestamp, determining a list of social keywordsassociated with a part of the social network thread corresponding to thetime interval, each social keyword being associated with a timestamp andvia analysis of descriptors for social network threads in the timeinterval, producing a filtered list of filtered multimedia keywords byselecting distinctive multimedia keywords, each filtered multimediakeyword being associated with a set of similar social keywordsidentified as having a similarity ratio above a predetermined threshold,computing a set of delays for at least one filtered multimedia keyword,each delay corresponding to the time difference between the timestamp ofthe filtered multimedia keyword and the timestamp of a similar socialkeyword associated with the filtered multimedia keyword, determining adrift for the time interval by means of at least one set of delays,wherein the drift for the time interval is determined as a function ofglobal delays related to filtered multimedia keywords, a global delayrelated to a filtered multimedia keyword being an average of a givennumber of delays associated with the filtered multimedia keyword.
 2. Themethod according to claim 1, wherein the time interval is selected afterhaving identified the peak in the social network thread, the timeinterval end corresponding to the top of the peak.
 3. The methodaccording to claim 2, wherein the peak is identified by a high variationof messages volume in the social network thread.
 4. The method accordingto claim 1, wherein a distinctive multimedia keyword has a low number ofoccurrences among the multimedia keywords in the corresponding timeinterval.
 5. The method according to claim 1, wherein a similar socialkeyword associated with a filtered multimedia keyword is a keyword thathas similar meaning or that is semantically close with respect to thefiltered multimedia keyword.
 6. The method according to claim 1, furthercomprising computing the delay for each similar social keyword in thelist of social keywords.
 7. The method according to claim 1, furthercomprising determining the drift for the time interval upon adetermination that the number of filtered multimedia keywords is greaterthan a predefined threshold.
 8. The method according to claim 1, furthercomprising determining the drift for the time interval upon adetermination that the number of delays associated with the filteredmultimedia keyword is greater than a given threshold and upon adetermination that the entropy of the delays distribution is low.
 9. Themethod according to claim 1, wherein the drift corresponds to an averageof all global delays related to the filtered multimedia keywords. 10.The method according to claim 1, wherein the list of multimedia keywordsassociated with the multimedia flow is determined after having extracteda multimedia fragment of the multimedia flow corresponding to the timeinterval, and after having analyzed content of the multimedia fragmentto generate a multimedia fragment descriptor containing multimediadescriptive elements.
 11. The method according to claim 1, wherein thelist of social keywords associated with the multimedia flow isdetermined after having analyzed a set of data in the social networkthread corresponding to the time interval to generate a threaddescriptor containing thread descriptive elements.
 12. The device forcontrolling synchronization between a multimedia flow and a relatedsocial network thread, the device being able to capture the multimediaflow and the social network thread and comprising: means for selecting atime interval, means for determining a list of multimedia keywordsassociated with a part of the multimedia flow corresponding to the timeinterval, each multimedia keyword being associated with a timestamp,means for determining a list of social keywords associated with a partof the social network thread corresponding to the time interval, eachsocial keyword being associated with a timestamp, means for producing afiltered list of filtered multimedia keywords by selecting distinctivemultimedia keywords, each filtered multimedia keyword being associatedwith a set of similar social keywords, means for computing a set ofdelays for at least one filtered multimedia keyword, each delaycorresponding to the time difference between the timestamp of thefiltered multimedia keyword and the timestamp of a similar socialkeyword associated with the filtered multimedia keyword, means fordetermining a drift for the time interval by means of at least one setof delays.
 13. A computer program stored on a non-transitorycomputer-readable medium in a device and configured to controlsynchronization between a multimedia flow and a related social networkthread, the device being configured to capture the multimedia flow andthe social network thread, the program comprising instructions which,once the program is loaded and executed within said device, carry outthe following: selecting a time interval as a function of a peakidentified in a social network thread, producing a list of multimediakeywords associated with a part of the multimedia flow corresponding tothe time interval, each multimedia keyword being associated with atimestamp, determining a list of social keywords associated with a partof the social network thread corresponding to the time interval, eachsocial keyword being associated with a timestamp and via analysis ofdescriptors for social network threads in the time interval, producing afiltered list of filtered multimedia keywords by selecting distinctivemultimedia keywords, each filtered multimedia keyword being associatedwith a set of similar social keywords identified as having a similarityratio above a predetermined threshold, computing a set of delays for atleast one filtered multimedia keyword, each delay corresponding to thetime difference between the timestamp of the filtered multimedia keywordand the timestamp of a similar social keyword associated with thefiltered multimedia keyword, determining a drift for the time intervalby means of at least one set of delays, wherein the drift for the timeinterval is determined as a function of global delays related tofiltered multimedia keywords, a global delay related to a filteredmultimedia keyword being an average of a given number of delaysassociated with the filtered multimedia keyword.